Sun Ting, Liu Jingfang, Wang Hui, Yang Bing Xiang, Liu Zhongchun, Liu Jie, Wan Zhiying, Li Yinglin, Xie Xiangying, Li Xiaofen, Gong Xuan, Cai Zhongxiang
Department of Nursing, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Health Science Center, Yangtze University, Jingzhou, People's Republic of China.
Neuropsychiatr Dis Treat. 2024 Aug 8;20:1539-1551. doi: 10.2147/NDT.S460021. eCollection 2024.
Non-suicidal self-injury (NSSI) is a significant social issue, especially among adolescents with major depressive disorder (MDD). This study aimed to construct a risk prediction model using machine learning (ML) algorithms, such as XGBoost and random forest, to identify interventions for healthcare professionals working with adolescents with MDD.
This study investigated 488 adolescents with MDD. Adolescents was randomly divided into 75% training set and 25% test set to testify the predictive value of risk prediction model. The prediction model was constructed using XGBoost and random forest algorithms. We evaluated the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, recall, F Score of the two models for comparing the performance of the two models.
There were 161 (33.00%) participants having NSSI. Compared without NSSI, there were statistically significant differences in gender (P=0.035), age (P=0.036), depressive symptoms (P=0.042), sleep quality (P=0.030), dysfunctional attitudes (P=0.048), childhood trauma (P=0.046), interpersonal problems (P=0.047), psychoticism (P) (P=0.049), neuroticism (N) (P=0.044), punishing and Severe (F2) (P=0.045) and Overly-intervening and Protecting (M2) (P=0.047) with NSSI. The AUC values for random forest and XGBoost were 0.780 and 0.807, respectively. The top five most important risk predictors identified by both machine learning methods were dysfunctional attitude, childhood trauma, depressive symptoms, F2 and M2.
The study demonstrates the suitability of prediction models for predicting NSSI behavior in Chinese adolescents with MDD based on ML. This model improves the assessment of NSSI in adolescents with MDD by health care professionals working. This provides a foundation for focused prevention and interventions by health care professionals working with these adolescents.
非自杀性自伤行为（NSSI）是一个重大的社会问题，在患有重度抑郁症（MDD）的青少年中尤为突出。本研究旨在使用机器学习（ML）算法，如XGBoost和随机森林，构建一个风险预测模型，以确定针对患有MDD的青少年的医疗保健专业人员的干预措施。
本研究调查了488名患有MDD的青少年。青少年被随机分为75%的训练集和25%的测试集，以验证风险预测模型的预测价值。使用XGBoost和随机森林算法构建预测模型。我们评估了两个模型的受试者工作特征曲线（AUC）下的面积、敏感性、特异性、准确性、召回率、F分数，以比较两个模型的性能。
有161名（33.00%）参与者存在NSSI。与无NSSI者相比，在性别（P = 0.035）、年龄（P = 0.036）、抑郁症状（P = 0.042）、睡眠质量（P = 0.030）、功能失调态度（P = 0.048）、童年创伤（P = 0.046）、人际问题（P = 0.047）、精神质（P）（P = 0.049）、神经质（N）（P = 0.044）、惩罚与严厉（F2）（P = 0.045）和过度干预与保护（M2）（P = 0.047）方面存在统计学显著差异。随机森林和XGBoost的AUC值分别为0.780和0.807。两种机器学习方法确定的前五个最重要的风险预测因素是功能失调态度、童年创伤、抑郁症状、F2和M2。
该研究证明了基于ML的预测模型适用于预测中国患有MDD的青少年的NSSI行为。该模型改善了医疗保健专业人员对患有MDD的青少年的NSSI评估。这为与这些青少年合作的医疗保健专业人员进行有针对性的预防和干预提供了基础。
